Demand-Driven Technologies for Sustainable Maize ... - IITA

36different germplasm and for placing inbred lines into heterotic groupsis the pedigree method. However, several approaches other than thepedigree method are available. For example, multivariate analysescould be used to identify closely related lines and determine thevariation in genetic similarity among genotypes that show no variationfor parentage, and to plan crosses between genetically divergentparents to maximize the genetic variation in segregating generations.Among the multivariate analytical methods, cluster analysis, principalcomponent analyses (PCA), principal coordinate analyses (PCoA), andmulti-dimensional scaling (MDS) are the most commonly employedand probably particularly useful (Melchinger 1993; Johns et al. 1997;Thompson et al. 1998; Brown-Guidera et al. 2000). The clusteringtechnique is very useful for the study of effects of pedigree and the originof genotypes on their phenotypic behavior in various environments(Shorter et al. 1977). Badu-Apraku et al. (2006b) used the phenotypicsimilarity (PS) based cluster analysis to study the genetic diversity in47 Striga resistant tropical early maize inbreds from WCA. Basedon the results of that study, it was predicted that heterosis could bebest maximized through matings between phenotypic-based clustersand by selecting parents which combine high grain yield, low Strigaemergence and damage symptoms.A study was initiated in 2004 to assess the genetic diversity in 36extra-early maturing maize inbred lines developed in the breedingprogram using UPGMA cluster analysis. The data collected on 31 outof 36 extra-early maturing Striga resistant inbred lines (26 with whiteendosperm color and 5 yellow endosperm color) at the S 6or S 7stageof inbreeding evaluated at Mokwa and Abuja under Striga-infested andnon-infested conditions in 2004 were used for the study. The details ofthe fi eld evaluation of the inbred lines have been described elsewherein this paper. Data for fi ve of the inbred lines used in the study were notusable and were therefore not included in the cluster analysis.Using the PRINCOMP procedure of the SAS package, principalcomponent analysis (PCA) was performed on the average values for eachtrait to identify a group of traits that accounted for most of the variancein the set of data and could be used to rank the inbred lines for theirperformance. The contribution of each trait to the principal componentaxis was determined by performing simple correlation analysis betweenthe PC scores and each trait. Only traits that had signifi cant correlationwith PC scores were considered as important and were selected for thecluster analysis. The cluster analyses were computed using the CLUSTERprocedure of the SAS statistical package.In the second study, combined analyses of variance of the data acrosslocations showed large genotypic variation in the phenotypic traits ofthe 31 inbred lines under Striga-infested and non-infested conditions